From 03d0b858c807339b4221bedffa29ac76eef5352e Mon Sep 17 00:00:00 2001 From: Matei Zaharia Date: Sun, 30 Jun 2013 15:38:58 -0700 Subject: Made use of spark.executor.memory setting consistent and documented it Conflicts: core/src/main/scala/spark/SparkContext.scala --- docs/tuning.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'docs/tuning.md') diff --git a/docs/tuning.md b/docs/tuning.md index 32c7ab86e9..5ffca54481 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -157,9 +157,9 @@ their work directories), *not* on your driver program. **Cache Size Tuning** -One important configuration parameter for GC is the amount of memory that should be used for -caching RDDs. By default, Spark uses 66% of the configured memory (`SPARK_MEM`) to cache RDDs. This means that - 33% of memory is available for any objects created during task execution. +One important configuration parameter for GC is the amount of memory that should be used for caching RDDs. +By default, Spark uses 66% of the configured executor memory (`spark.executor.memory` or `SPARK_MEM`) to +cache RDDs. This means that 33% of memory is available for any objects created during task execution. In case your tasks slow down and you find that your JVM is garbage-collecting frequently or running out of memory, lowering this value will help reduce the memory consumption. To change this to say 50%, you can call -- cgit v1.2.3